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Automated Generation of Executable RPA Scripts from User Interface Logs

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Business Process Management: Blockchain and Robotic Process Automation Forum (BPM 2020)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 393))

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Abstract

Robotic Process Automation (RPA) operates on the user interface (UI) of software applications and automates - by means of a software (SW) robot - mouse and keyboard interactions to remove intensive routine tasks (or simply routines). With the recent advances in Artificial Intelligence, the automation of routines is expected to undergo a radical transformation. Nonetheless, to date, the RPA tools available in the market are not able to automatically learn to automate such routines, thus requiring the support of skilled human experts that observe and interpret how routines are executed on the UIs of the applications. Being the current practice time-consuming and error-prone, in this paper we present SmartRPA, a cross-platform tool that tackles such issues by exploiting UI logs to automatically generate executable RPA scripts that automate the routines enactment by SW robots.

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Notes

  1. 1.

    https://palletsprojects.com/p/flask.

  2. 2.

    https://pandas.pydata.org/.

  3. 3.

    XES is the standard for the storage, interchange, and analysis of event logs [15].

  4. 4.

    http://www.promtools.org/.

  5. 5.

    https://fluxicon.com/disco/.

  6. 6.

    https://apromore.org/.

  7. 7.

    https://github.com/automagica/automagica.

  8. 8.

    https://www.selenium.dev/.

References

  1. van der Aalst, W.M.P., Bichler, M., Heinzl, A.: Robotic process automation. Bus. Inf. Syst. Eng. 60(4), 269–272 (2018)

    Google Scholar 

  2. Agostinelli, S., Maggi, F.M., Marrella, A., Milani, F.: A user evaluation of process discovery algorithms in a software engineering company. In: 2019 IEEE 23rd International Enterprise Distributed Object Computing Conference (EDOC), pp. 142–150 (2019). https://doi.org/10.1109/EDOC.2019.00026

  3. Agostinelli, S., Marrella, A., Mecella, M.: Research challenges for intelligent robotic process automation. In: Di Francescomarino, C., Dijkman, R., Zdun, U. (eds.) BPM 2019. LNBIP, vol. 362, pp. 12–18. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-37453-2_2

    Chapter  Google Scholar 

  4. Agostinelli, S., Marrella, A., Mecella, M.: Towards Intelligent Robotic Process Automation for BPMers (2020). http://arxiv.org/abs/2001.00804

  5. Aguirre, S., Rodriguez, A.: Automation of a business process using robotic process automation (RPA): a case study. In: Figueroa-García, J.C., López-Santana, E.R., Villa-Ramírez, J.L., Ferro-Escobar, R. (eds.) WEA 2017. CCIS, vol. 742, pp. 65–71. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66963-2_7

    Chapter  Google Scholar 

  6. AI-Multiple: All 52 RPA Software Tools & Vendors of 2020: Sortable List (2019). https://blog.aimultiple.com/rpa-tools/

  7. Ayub, A., Wagner, A.R.: Teach Me What You Want to Play: Learning Variants of Connect Four through Human-Robot Interaction (2020). https://arxiv.org/abs/2001.01004

  8. Berti, A., van Zelst, S.J., van der Aalst, W.: Process Mining for Python (PM4Py): Bridging the Gap Between Process- and Data Science (2019). http://arxiv.org/abs/1905.06169

  9. Bisbal, J., Lawless, D., Wu, B., Grimson, J.: Legacy information systems: issues and directions. IEEE Softw. 16(5), 103–111 (1999)

    Article  Google Scholar 

  10. Bosco, A., Augusto, A., Dumas, M., La Rosa, M., Fortino, G.: Discovering automatable routines from user interaction logs. In: Hildebrandt, T., van Dongen, B.F., Röglinger, M., Mendling, J. (eds.) BPM 2019. LNBIP, vol. 360, pp. 144–162. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-26643-1_9

    Chapter  Google Scholar 

  11. Gao, J., van Zelst, S.J., Lu, X., van der Aalst, W.M.P.: Automated robotic process automation: a self-learning approach. In: Panetto, H., Debruyne, C., Hepp, M., Lewis, D., Ardagna, C.A., Meersman, R. (eds.) OTM 2019. LNCS, vol. 11877, pp. 95–112. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-33246-4_6

    Chapter  Google Scholar 

  12. Geyer-Klingeberg, J., Nakladal, J., Baldauf, F., Veit, F.: Process mining and robotic process automation: a perfect match. In: 16th International Conference on Business Process Management (BPM 2018), Dissertation/Demos/Industry track (2018)

    Google Scholar 

  13. Han, X., et al.: Automatic Business Process Structure Discovery using Ordered Neurons LSTM: A Preliminary Study (2020). https://arxiv.org/abs/2001.01243

  14. Hill, J., Ford, W.R., Farreras, I.G.: Real conversations with artificial intelligence: a comparison between human-human online conversations and human-chatbot conversations. Comput. Hum. Behav. 49, 245–250 (2015)

    Article  Google Scholar 

  15. IEEE Digital Library: Standard for eXtensible Event Stream (XES) for Achieving Interoperability in Event Logs and Event Streams. IEEE Std 1849–2016 (2016). https://doi.org/10.1109/IEEESTD.2016.7740858

  16. Ito, N., Suzuki, Y., Aizawa, A.: From natural language instructions to complex processes: issues in chaining trigger action rules (2020). https://arxiv.org/abs/2001.02462

  17. Jenkins, P., Wei, H., Jenkins, J.S., Li, Z.: A Probabilistic Simulator of Spatial Demand for Product Allocation (2020). https://arxiv.org/abs/2001.03210

  18. Jimenez-Ramirez, A., Reijers, H.A., Barba, I., Del Valle, C.: A method to improve the early stages of the robotic process automation lifecycle. In: Giorgini, P., Weber, B. (eds.) CAiSE 2019. LNCS, vol. 11483, pp. 446–461. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-21290-2_28

    Chapter  Google Scholar 

  19. Kirchmer, M.: Robotic Process Automation-Pragmatic Solution or Dangerous Illusion. BTOES Insights, June 17 (2017)

    Google Scholar 

  20. Le, V., Gulwani, S.: FlashExtract: a framework for data extraction by examples. In: ACM SIGPLAN PLDI 2014, pp. 542–553 (2014)

    Google Scholar 

  21. Leno, V., Polyvyanyy, A., Rosa, M.L., Dumas, M., Maggi, F.M.: Action logger: enabling process mining for robotic process automation. In: Proceedings of the Dissertation Award, Doctoral Consortium, and Demonstration Track at 17th International Conference on Business Process Management (BPM 2019), pp. 124–128 (2019)

    Google Scholar 

  22. Leopold, H., van der Aa, H., Reijers, H.A.: Identifying candidate tasks for robotic process automation in textual process descriptions. In: Gulden, J., Reinhartz-Berger, I., Schmidt, R., Guerreiro, S., Guédria, W., Bera, P. (eds.) BPMDS/EMMSAD -2018. LNBIP, vol. 318, pp. 67–81. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-91704-7_5

    Chapter  Google Scholar 

  23. Levenshtein, V.: Efficient implementation of the levenshtein-algorithm, fault-tolerant search technology, error-tolerant search technologies (2007). http://www.levenshtein.net/

  24. Linn, C., Zimmermann, P., Werth, D.: Desktop activity mining - A new level of detail in mining business processes. In: Workshops der INFORMATIK 2018 - Architekturen, Prozesse, Sicherheit und Nachhaltigkeit, September 26–27, pp. 245–258 (2018)

    Google Scholar 

  25. Marrella, A., Mecella, M., Sardiña, S.: Supporting adaptiveness of cyber-physical processes through action-based formalisms. AI Commun. 31(1), 47–74 (2018). https://doi.org/10.3233/AIC-170748

    Article  MathSciNet  Google Scholar 

  26. Miltner, A., et al.: On the fly synthesis of edit suggestions. ACM Program. Lang. 3(OOPSLA), 1–29 (2019)

    Article  Google Scholar 

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Acknowledgments

This work has been supported by the “Dipartimento di Eccellenza” grant, the H2020 projects DESTINI and FIRST, the Italian project RoMA - Resilience of Metropolitan Areas, and the Sapienza grant BPbots.

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Correspondence to Andrea Marrella .

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Agostinelli, S., Lupia, M., Marrella, A., Mecella, M. (2020). Automated Generation of Executable RPA Scripts from User Interface Logs. In: Asatiani, A., et al. Business Process Management: Blockchain and Robotic Process Automation Forum. BPM 2020. Lecture Notes in Business Information Processing, vol 393. Springer, Cham. https://doi.org/10.1007/978-3-030-58779-6_8

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  • DOI: https://doi.org/10.1007/978-3-030-58779-6_8

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